transfer : transactions routing for ad-hoc networks with efficient energy

Ahmed Helmy - UFL 1

TRANSFER: Transactions Routing for Ad-hoc NetworkS with eFficient EneRgy

Ahmed HelmyComputer and Information Science and Engineering (CISE)

University of Florida (UFL)

email: [email protected]

web: www.cise.ufl.edu/~helmy

Wireless Networking Lab: nile.cise.ufl.edu

Ahmed Helmy - UFL 2

Motivation• Most current ad hoc routing approaches

– Setup/maintain optimal (e.g., shortest) routes (DSR, AODV, ZRP,..)

– Incur high route discovery cost, warranted for long-lived flows where cost is amortized over flow duration

• In Small Transactions – Cost is dominated by route discovery (vs. data transfer)

• Design Goal: reduce cost for small transactions

• Example small transactions: resource discovery query, text messaging, sensor network query, etc.

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Approach

• Avoid flooding-based approaches and instead of flat architecture use hierarchical architecture

• Instead of complex hierarchy formation use loose hierarchy (zone-based)

• Instead of bordercasting (as in ZRP) query only a few selected contact nodes– Contacts act as short cuts to bridge zones and reduce

degrees of separation between querier & resource– Borrows from the concept of small worlds

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sensor

sensorsensor

sensor

sensorsensor

sensor

sensor

Pocket PC

PDA

Mobilephone

Handheld

Computing capability

GPS & location capability


Computing capabilitySource (S)

Target

Zone of S

Contact (C1)

Contact (C2)

Zone of C1

Zone of C2

sensor

sensorsensor

sensor

sensorsensor

sensor

sensor

Pocket PC

PDA

Mobilephone

Handheld

Computing capability

GPS& location capability


Computing capabilitySource (S)

Target

(a) Flooding from source (S) to discover Target (b) Query from source (S) using contacts C1 and C2 to

discover Target

Flooding vs. Contact-based Query

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R

R

R

R

Q

contact

contact

contact

Q: Querier Node B: Border Node C: Contact Node R: Proximity radius r: Contact distance

r

C2

C1

C3

B1

B2

B3

C2’sproximity

Q’sproximity

Architectural Overview

NoC: Number of Contacts

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Contact Selection Scheme

• Reactive (on-demand) contact selection

• Choose contacts with reduced proximity overlap

• Proximity overlap reduction mechanisms– use the proximity information at the border (if

available as link state) to reduce the overlaps– use the neighbor-neighbor avoidance mechanism– use disjoint paths (as possible) to reach contacts

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RR

Q

contact

Q: Querier NodeB: Border Node

C: Contact NodeR: zone radius

B

C1

R

xL

R

R

Q

contact

tr: transmission trange

tr

L

BC2

R

x

y

z

B avoids going through L’s neighbors x, y, z(Straightening algorithm)

Overlap Problem and Solution

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Search Policies• Levels of contacts defined by maximum depth D

• Several search policies investigated:– Single-shot uses 1 attempt (minimum latency)– Level-by-level uses several attempts with depth level

increased by 1 for every attempt– Step uses several attempts with depth increased

exponentially 1,2,4,8,… (minimum overhead)

• In multi-attempts use the rotation effect– choose different level-1 contacts for different attempts to

increase network coverage

• Use loop detection and re-visit prevention

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Q

contact-1

contact-1

contact-1

contact-2contact-2

contact-2

contact-2

contact-2

contact-2

contact-2

contact-2

contact-2

Single-shot Policy

NoC=3D=2R=3r=3

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contact-1

contact-1

contact-1

Qcontact-2

contact-1

contact-2

contact-2

contact-1

contact-2contact-2

contact-2

contact-1

contact-2

contact-2

contact-2

Level-by-levelor Step Policy

NoC=3D=2R=3r=3


Attempt 1

Attempt 1

Attempt 1

Attempt 2

Attempt 2

Attempt 2

Attempt 3

Attempt 3

Attempt 3

Q

Rotation-like effect in the step search policy


Evaluation and Analysis

• Trade-off between success rate vs. energy

• Simulation uses fallback to flooding upon failure

• Parameter analysis (optimum r, NoC, D)

• Main evaluation metric is total energy consumption

• Energy consumption due to various components– Proximity maintenance: function of mobility m/s– Query overhead: function of query rate query/s– Total Consumption: function of q (query/s)/(m/s) QMR


The Communication Energy Model Based on IEEE 802.11

Accounts for energy consumption due to transmission and reception

Accounts for differences between broadcast and unicast messages

Energy consumed by a broadcast message (Eb):– Eb=Etx+g.Erx=Etx(1+f.g), where g is ave. node degree.

Energy consumed by a unicast message (Eu):– Eu=Etx+Erx+Eh=Etx(1+f+h), where f=Erx/Etx and h=Eh/Etx, Eh energy

consumption due to handshake.

• For this study we use f=0.64, and h=0.1


Nodes Area (mxm) Node Degree Border Nodes Proximity Nodes200 1000x1000 7.6 15.1 35500 1400x1400 8.9 20.5 44.81000 2000x2000 9.1 21.7 46.82000 2800x2800 9.7 24.7 52.94000 3700x3700 11 30.3 62.28000 4800x4800 13 38.8 77.816000 6500x6500 14.3 44.6 88.232000 9200x9200 14.3 45 88.9

Simulation Setup• Random node layout

• Random way point mobility model [0,20] m/s

• Random src-dst pair selection

• R=3 to limit storage and proximity overhead


Optimum Number of Contacts (NoC)

, r=3, D=33 (5 attempts max)

N=1000 nodes

Reduced coveragefrequent fallback to flooding

Increased query threads

- Optimum NoC=3, resulting in (near) perfect coverage


Optimum contact distance (r)

, NoC=3, D=33 (5 attempts max)

N=1000 nodes

- Optimum r=3, resulting in min overlap and max coverage


Optimum depth of search (D)

3 attempts

2 attempts

4 attempts 5 attempts

- D=33 (5 attempts max) results in (near) perfect coverage- High order attempts (4th & 5th) only search unvisited partsof the network (due to re-visit prevention) and achieve increased coverage without excessive overhead

, NoC=3, r=3

N=1000 nodes


(1) Per-Query Energy Consumption

Scalability Analysis and Comparisons

(NoC=3, r=3, D=33)- Total query energy consumption = f(query rate) query/s- Define per-query energy as Estep, Eflood and Eborder


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50

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Network Size (nodes)

Ene

rgy

pe

r n

od

e p

er

se

c p

er

m/s Z(3)

Z(5)

(2) Proximity (Zone) Maintenance Energy Consumption

Comparisons (contd.)

- For TRANSFER Z(R)=Z(3), for ZRP Z(2R-1)=Z(5) (extended zone)- Proximity cost=f(mobility) m/s



– To combine the proximity energy, f(mobility), and the query energy, f(query rate)

– The query-mobility-ratio (QMR) metric, q, in query/s/(m/s) is used for normalization

• Total Step Energy: ETstep=Z(R)+q.Estep

• Total Flood Energy: ETflood=q.Eflood

• Total ZRP Energy: ETborder=Z(2R-1)+q.Eborder

– Define total energy ratios (TER):

flood

step

Tflood

Tstepflood Eq

EqRZ

E

ETER

.

.)(

border

step

Tborder

Tstepborder EqRZ

EqRZ

E

ETER

.)12(

.)(

Total Energy Consumption: Proximity + Query Energy


(3.a) Total Energy Consumption (vs. Flooding)


- For high query rates achieves energy savings of 90-95% over flooding


(3.b) Total Energy Consumption (vs. ZRP bordercasting)


- For high query rates achieves energy savings of 75-86% over ZRP


Summary/ Conclusions• Developed a contact-based architecture for

energy-efficient routing of small transactions

• Introduced effective contact selection scheme

• Investigated several search policies (e.g., Step)

• Analyzed performance of TRANSFER and showed favorable parameter settings for a wide array of networks

• Achieved gains for high query rates 75-95% as compared to flooding and ZRP


Backup Slides


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ODC: on-demand routing with caching (DSR-like)MDS: minimum dominating set algorithmSmart-fld: smart flooding (location-based heuristic)


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ODC: on-demand routing with caching (DSR-like)MDS: minimum dominating set algorithmSmart-fld: smart flooding (location-based heuristic)

transfer : transactions routing for ad-hoc networks with efficient energy

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